Abstract: To make it easier to perform an in-depth analysis by presenting to a user which feature value influences and how it influences an objective variable in a case of automatically generating feature values. For each of a plurality of feature values, by determining a division point indicating a change in influence on an objective variable, each feature value is divided into a plurality of segments and an evaluation value is calculated using an influence degree of each segment on the objective variable as an index. The segments of the feature values for which the evaluation values have been calculated are displayed on a monitor.

Abstract: To enable effectively narrowing down features to be generated, thereby generating effective features at a high speed, in obtaining the features from a large volume of data. A fixed rule and an additional rule are stored in advance. The fixed rule specifies a rule of a calculation operation for generating a new feature. The additional rule specifies whether to perform a calculation operation for generating the new feature on a basis of meta-information, not depending on whether the fixed rule is applicable. An objective variable is predicted from plurality of features on the basis of the fixed rule and the additional rule.

Abstract: A data analyzing device generates a diagram that shows a relationship between a first feature and an objective variable. The first feature is selected in accordance with an input of a user from among features having higher degrees of importance. The data analyzing device divides analysis target data into a plurality of clusters on the basis of values of the first feature, calculates a representative value of the objective variable of each of the clusters, extracts a second feature having a representative value of the objective variable, which is determined as having a significant difference relative to the representative value of the objective variable of the first feature, from at least one of the clusters. The data analyzing device generates a diagram that shows a relationship between the second feature and the objective variable.

Abstract: To make it easier to perform an in-depth analysis by presenting to a user which feature value influences and how it influences an objective variable in a case of automatically generating feature values. For each of a plurality of feature values, by determining a division point indicating a change in influence on an objective variable, each feature value is divided into a plurality of segments and an evaluation value is calculated using an influence degree of each segment on the objective variable as an index. The segments of the feature values for which the evaluation values have been calculated are displayed on a monitor.

Abstract: A data analyzing device generates a diagram that shows a relationship between a first feature and an objective variable. The first feature is selected in accordance with an input of a user from among features having higher degrees of importance. The data analyzing device divides analysis target data into a plurality of clusters on the basis of values of the first feature, calculates a representative value of the objective variable of each of the clusters, extracts a second feature having a representative value of the objective variable, which is determined as having a significant difference relative to the representative value of the objective variable of the first feature, from at least one of the clusters. The data analyzing device generates a diagram that shows a relationship between the second feature and the objective variable.

Abstract: To enable effectively narrowing down features to be generated, thereby generating effective features at a high speed, in obtaining the features from a large volume of data. A fixed rule and an additional rule are stored in advance. The fixed rule specifies a rule of a calculation operation for generating a new feature. The additional rule specifies whether to perform a calculation operation for generating the new feature on a basis of meta-information, not depending on whether the fixed rule is applicable. An objective variable is predicted from plurality of features on the basis of the fixed rule and the additional rule.